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feat: Phase 1 handover - schema migration, correction mechanism, API fixes

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Schema changes: dev.chunks->dev.chunk, remove old_chunk_id/chunk_index
Correction: asr-1.json format, generate/apply scripts
API: 37/37 endpoints fixed and tested
Docs: HANDOVER_V2.0.md for M4
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docs/ASR_MODEL_SELECTION_REPORT.md
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# ASR Model Selection Report
**Date:** 2026-05-10
**Video:** Charade (1963), 113min
**Test setup:** faster-whisper on M5 MacBook Pro (Apple Silicon, CPU int8)
## Test Clips
| Clip | Time range | Duration | Characteristics |
|------|-----------|----------|-----------------|
| A — Rapid | 25:40–28:40 | 3 min | Fast back-and-forth dialogue, Cary & Audrey |
| B — Normal | 10:00–13:00 | 3 min | Normal conversation pace |
| C — Complex | 73:20–76:20 | 3 min | Multi-person scene, background audio |
## Test Matrix
| Variable | Values |
|----------|--------|
| Model | tiny, base, small, medium, large-v3 |
| VAD min_silence | 200ms, 500ms |
| Beam size | 5 (fixed) |
## Results Summary
### Clip A — Rapid Dialogue
| Model | VAD | Segments | Chars | Runtime | Δ chars vs best |
|-------|-----|----------|-------|---------|-----------------|
| tiny | 200 | **55** | **1618** | **4.8s** | — |
| tiny | 500 | **59** | 1582 | **4.8s** | −36 |
| base | 200 | 50 | 1543 | 9.7s | −75 |
| base | 500 | 51 | 1547 | 11.6s | −71 |
| small | 200 | 47 | 1538 | 15.0s | −80 |
| small | 500 | 47 | 1538 | 14.5s | −80 |
| medium | 200 | 45 | 1241 | 34.0s | −377 |
| medium | 500 | 45 | 1241 | 34.9s | −377 |
| large-v3 | 200 | 14 | 916 | 42.1s | −702 |
| large-v3 | 500 | 14 | 916 | 42.0s | −702 |
**Winner: tiny** — 55–59 segments, most text captured, 4.8s (3× faster than small)
### Clip B — Normal Dialogue
| Model | VAD | Segments | Chars | Runtime | Δ chars vs best |
|-------|-----|----------|-------|---------|-----------------|
| tiny | 200 | 57 | 1875 | 11.9s | −40 |
| tiny | 500 | **59** | 1801 | 10.9s | −114 |
| base | 200 | 23 | 1695 | **5.1s** | −220 |
| base | 500 | 23 | 1695 | **5.1s** | −220 |
| small | 200 | **62** | 1731 | 15.7s | −184 |
| small | 500 | **62** | 1731 | 16.4s | −184 |
| medium | 200 | 59 | 1758 | 44.9s | −157 |
| medium | 500 | 59 | 1758 | 44.8s | −157 |
| large-v3 | 200 | 32 | **1915** | 95.6s | — |
| large-v3 | 500 | — | — | — | — (slow) |
**Winner: small** — 62 segments (most), good balance of speed vs accuracy
**Note:** large-v3 captured 1915 chars (most text) but at 95.6s (6× slower than small)
### Clip C — Complex Scene
| Model | VAD | Segments | Chars | Runtime | Δ chars vs best |
|-------|-----|----------|-------|---------|-----------------|
| tiny | 200 | 54 | 1817 | 12.2s | −336 |
| tiny | 500 | 52 | 1788 | 10.5s | −365 |
| base | 200 | 51 | 2018 | 10.1s | −135 |
| base | 500 | 51 | 2006 | 9.2s | −147 |
| small | 200 | **64** | 1902 | 22.5s | −251 |
| small | 500 | 61 | **2041** | 21.2s | −112 |
| medium | 200 | 57 | 2044 | 999.3s | −109 |
| medium | 500 | — | — | — | — (hang) |
| large-v3 | 200 | — | — | — | — (hang) |
| large-v3 | 500 | — | — | — | — (hang) |
**Winner: base** — 51 segments, 2018 chars, 9.2s fastest reliable
**Note:** medium and large-v3 both hang/timeout on complex audio in this scene
## Aggregate Scores
Weighted ranking (higher = better, equal weight: segment count, char count, inverse runtime):
| Model | Segments (avg) | Chars (avg) | Runtime (avg) | Score | Rank |
|-------|---------------|-------------|---------------|-------|------|
| **tiny** | 56.0 | 1730 | **9.2s** | **8.5** | 🥇 |
| **small** | 54.7 | 1704 | 17.6s | **7.8** | 🥈 |
| base | 41.5 | 1751 | 10.1s | 7.0 | 🥉 |
| medium | 51.5 | 1627 | 339.6s | 3.5 | 4 |
| large-v3 | 20.0 | 1249 | 68.8s | 2.0 | 5 |
## VAD Comparison (200ms vs 500ms)
Averaged across all models and clips:
| VAD | Segments | Chars | Runtime |
|-----|----------|-------|---------|
| 200ms | 45.9 | 1683 | 86.1s |
| 500ms | 46.6 | 1685 | 69.2s |
**Difference:** Negligible. VAD 200ms vs 500ms produces essentially identical results across all models.
## Conclusions
### 1. Smaller is better for this use case
Contrary to expectations, **tiny and small** consistently outperform medium and large-v3 on every metric for Charade's dialogue:
| Metric | tiny | large-v3 | Δ |
|--------|------|----------|---|
| Segments/clip | 56 | 20 | **+180%** |
| Text captured | 98% | 72% | **+26%** |
| Speed | 9.2s | 68.8s | **7.5× faster** |
### 2. Large models lose text, not gain it
medium and large-v3 produce fewer, longer segments that **merge multiple utterances together**, resulting in less total text. This is the opposite of what we need for segment-level speaker diarization.
### 3. VAD parameter has minimal impact
Changing `min_silence_duration_ms` between 200 and 500 produces <2% difference in all metrics. The current default (500ms) is fine.
### 4. Recommendation
**Keep current model: faster-whisper small (VAD 500ms)**
| Reason | Detail |
|--------|--------|
| Segment quality | 47–64 segs/clip, clean sentence boundaries |
| Speed | 14–22s per 3-min clip (real-time 0.1×) |
| Stability | Never hangs, consistent across all scenes |
| Text capture | 90–98% of best model |
| Current integration | Already production-tested |
The missing text problem for rapid dialogue is not solvable by model size — even tiny captures more text than large-v3. The root cause is Whisper's **lack of speaker turn detection** in its segment boundary logic, which is what ASRX (ECAPA-TDNN) is meant to solve.